The manufacturing floor environment is continuously evolving and to ensure that the product meets the required quality and safety standards, manufacturers must continuously identify optimal machine parameters and manufacturing layout. Unfortunately, the current solutions on the market are rather fragmented and costing companies millions of dollars in losses due to scrap, warranty, and liability. Current methods include either visual inspections which are only conducted at a point in time and are highly subjective or ML and analytic solutions that without subject matter experts cannot keep up with the intricate behaviors and changing environments. Moreover, unreliable or bad quality parts could quickly ruin manufacturer’s brand and reputation.
Eigen innovation leverages insights from across the factory floor to improve process efficiencies at a machine by machine level and improve quality assurance. The solution leverages deep learning machine algorithms on factory floor videos and images to detect otherwise undetectable defects while monitoring the production in order to correlate defects with machines’ KPIs. This enables manufacturer to start a continuous feedback loop that refines and optimizes production towards a zero-defect goal. It also allows operator to provide input in real-time.